For 2019, we present

• the Metagenomic Forensic Challenge – now with multiple data sources!
• the Hi-Res Cancer Data Integration Challenge – now with 300Mb of novel human genomic sequence and read level data for splice events!
• the CMap Drug Safety Challenge – now with more cell-lines, high-content screens, and chemical structures!

CAMDA encourages an open contest, where all analyses of the contest data sets are of interest, not limited to the questions suggested here. There is an online forum for the free discussion of the contest data sets and their analysis, in which you are encouraged to participate.

We look forward to a lively contest!

MetaSUB is building an international metagenomic map of urban spaces, based on extensive sampling of mass-transit systems and other public areas across the globe. In a strategic partnership new data from global City Sampling Days is first introduced through the annual CAMDA contests. CAMDA delegates thus receive access to hundreds of novel MetaSUB samples, comprising several gigabases of whole genome shotgun (WGS) metagenomics data. The primary data set covers multiple cities around the world, with tens of samples per city, providing a unique resource for the study of biodiversity within and across geographic locations.

Further extended global coverage is achieved by complementary 16S rRNA studies contributing thousands of samples, the Earth Microbiome Project and A global atlas of the dominant bacteria found in soil. For a range of MetaSUB Boston reference samples we now provide both WGS and 16S profiles, allowing a first systematic link of WGS and 16S resources.

Together, these unique resources support novel approaches to identify sample origin locations for cities seen for the very first time. Performance can be tested on an independent test set of over 50 samples from multiple 'mystery' locations from cities not sampled before.

Please visit and participate in the open CAMDA meta-genomics forum for free discussion related to this contest.

From the comprehensive description of genomic, transcriptomic and epigenomic changes of cancers provided by Genomic Data Commons (GDC, formerly at TCGA), the main goal of this challenge is to develop and demonstrate novel methods for gaining novel biological insights or improving support for Precision Medicine. Innovation can come from

• Individual human genomic sequence not found in the standard reference genome - We provide reads matched to the standard reference genome plus over 300Mb of novel human genomic sequence.
• High resolution expression profiling - Anonymized read level data allow the exploration of aberrations in splicing and regulation of alternative gene transcripts.
• More meaningful integration of multiple matched molecular profiles and complementary patient data

Examine algorithm performance in a real-world clinical settings! We know that many approaches work well on some data-sets yet not on others. We here challenge you to demonstrate a unified single approach that matches or outperforms the current state-of-the-art for

• Breast cancer

and for at least one of the less well studied

• Lung Adenocarcinoma
• Kidney Renal Clear Cell Carcinoma

Please visit and participate in the open CAMDA data integration forum for free discussion related to this contest.

Attrition in drug discovery and development due to safety / toxicity issues remains a significant concern, and there are strong efforts to identify and mitigate risk as early as possible. Drug-induced liver injury (DILI) is one of the primary problems in drug development and regulatory clearance due to the poor performance of existing preclinical models. There is a pressing need to evaluate alternative methods for predicting DILI, with great hopes being placed in modern approaches from statistics and machine learning applied to genome scale profiling data. A critical question thus is if we can better integrate, understand, and exploit information from cell-based screens like the Broad Institute Connectivity Map (CMap, Science 313, Nature Reviews Cancer 7, Cell 171).

This CAMDA challenge focuses on understanding or predicting drug induced liver injury in humans from cell-based screens, specifically the CMap L1000 gene expression responses of thirteen different cancer cell lines to 1,314 drug compounds, greatly extending last year's challenge data set (common cell lines: MCF7 & PC3, new cell lines: A375, A549, ASC, HA1E, HCC515, HEPG2, HT29, NPC, PHH, SKB, VCAP). To also support supervised approaches, we provide clinical DILI results as training labels for 233 drugs (21 high concern, 62 less concern, 35 no concern, and 55 ambiguous) .

In addition, we now provide chemical structures (SMILES codes) for all drugs, and annotated images from cellular assays (Journal of chemical information and modeling (to appear), Nature Protocols 11, GigaScience 6) for a subset of drugs and cell lines. Please read and accept the data download agreement for access. Please visit and participate in the open CAMDA toxicogenomics forum for free discussion related to this contest.